Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA293702 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:DNA methylation-based immune response signature improves patient diagnosis in multiple cancers (Cohort 2)
| PRJNA293703 | ENA
Project description:DNA methylation-based immune response signature improves patient diagnosis in multiple cancers (Cohort 1)
| PRJNA293704 | ENA
Project description:DNA methylation-based immune response signature improves patient diagnosis in multiple cancers
| PRJNA293700 | ENA
Project description:Triple-Negative Breast Cancer (TNBC) is a heterogeneous collection of cancers where personalized treatment is difficult and chemotherapy and immunotherapy combinations are the main treatment options. Many attempts to tackle patient heterogeneity have focused on defining cancer-intrinsic subtypes based on differential tumor mRNA-expression across patient cohorts. While these multi-gene diagnostics have shown success in hormone receptor- positive cancers (e.g. OncotypeDX), no TNBC classifiers have shown clinical utility in predicting patient survival or treatment response. We hypothesize that TNBC-infiltrating immune-cells both affect mRNA-based classification and contribute to treatment response variability. To evaluate this hypothesis, we benchmarked the performance of common TNBC-classification (TNBC-type) and infiltrating immune (CIBERSORT) algorithms on the same underlying datasets. Encouragingly, we found that – as with OncotypeDx– highly proliferative TNBC-subtypes (BL1) show the strongest evidence of response to cytotoxic chemotherapies. Interestingly, this cancer-proliferative signature (BL1) is strongly correlated with enrichment in tumor-infiltrating lymphocyte signatures (TIL) which show superior prognostic and predictive power. In addition, Tumor Associated Macrophage (TAM) signatures show independent predictive and prognostic power for both patient survival and response to anthracycline- and taxane-based chemotherapies. These gene signature-based correlations were validated in a new independent cohort of 67 TNBC-patients treated with neoadjuvant chemotherapy. Overall, these results argue for the independent contributions of both cancer-intrinsic and -extrinsic factors in predicting treatment response in the neoadjuvant setting.
2024-03-20 | GSE260693 | GEO
Project description:Lynch syndrome (LS) patients develop DNA mismatch repair deficient tumors which generate high loads of neoantigens (neoAgs), thus constituting a well-defined population that can benefit from cancer immune-interception strategies, including neoantigen-based vaccines. Using paired whole-exome sequencing and mRNAseq of colorectal cancers (CRC) (n=13) and pre-cancers (n=61) from our LS patient cohort (N=46), we performed in-silico prediction and immunogenicity ranking of highly recurrent frameshift-neoags, followed by their in-vitro validation. We described the somatic mutation landscape in all cancers and pre-cancers, and showed that mutation burden is positively correlated with neoAgs load. Furthermore, our in-vitro validation showed a 65% validation rate of our top 100 predicted neoags. Consistent with neoAgs burden, our transcriptomic results revealed increased infiltration of CD8+ and CD4+ T-cells in microsatellite unstable samples. Overall, our neoAgs catalog and all other findings, improve our understanding of cancer development in LS and guide us towards the advancement of immunoprevention vaccine strategies.
2024-03-25 | GSE224707 | GEO
Project description:Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis of all common cancers, but divergent outcomes are apparent between patients. To delineate the intertumor heterogeneity that contributes to this, we aimed to identify clinically distinct gene expression-based subgroups. From a cohort of 345 resected pancreatic cancer cases, 90 samples with confirmed diagnosis of PDAC and sufficient tumor content were available for gene expression analysis by RNA sequencing. Unsupervised classification was applied, and a classifier was constructed. Species-specific transcript analysis on matching patient-derived xenografts (PDX, N=14) allowed construction of tumor- and stroma-specific classifiers for use on PDX models and cell lines.
2020-01-25 | E-MTAB-6830 | biostudies-arrayexpress
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Experiment Overall Design: We used microarrays to identify lung metastasis-related genes in a series of 23 patients with breast cancer metastases. No replicate, no reference sample.
2009-03-07 | E-GEOD-11078 | biostudies-arrayexpress
Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort. Profiling of 70 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. All tumors were collected with approvals from Peter MacCallum Cancer Center, Australia; the Research Ethics Review Committee; and signed patient informed consent.
2012-07-04 | E-GEOD-35809 | biostudies-arrayexpress
Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort.
2012-07-05 | GSE35809 | GEO
Project description:Establishing reliable biomarkers for assessing and validating clinical diagnosis at early prodromal stages of Parkinson’s disease is crucial for developing therapies to slow or halt disease progression. Here, we present the largest study to date using whole blood gene expression profiling from over 500 individuals to identify an 87-gene blood-based signature. Our gene signature effectively differentiates between idiopathic PD patients and controls in both a validation cohort and an independent test cohort, and further highlights mitochondrial metabolism and ubiquitination/proteasomal degradation as potential pathways disrupted in Parkinson’s disease.
2017-05-20 | GSE99039 | GEO